Comparison of the temporal evolution of satellite-derived deep convective cloud properties for severe vs. non-severe convection

Geostationary satellite observations are an effective tool for quantifying the macro- and microphysical properties of deep convective clouds as a function of time. Various cloud properties can be derived from geostationary weather satellites, such as the Geostationary Operational Environmental Satellite (GOES), including: cloud top temperature (day and night), cloud top height (day and night), cloud top phase (day and night), visible cloud optical depth (day), cloud effective particle radius (day and night, with additional limitations at night), and cloud emissivity (day and night) at high temporal resolution (five to fifteen minute). These cloud properties, some of which can be fully retrieved throughout the diurnal cycle, can then be tracked in time to provide insight into the micro- and mesoscale characteristics of different types of convection. By treating individual clouds as objects and tracking them over time through space, we are able to capture and study the unique temporal trends in the above satellite-derived cloud properties of individual objects. In particular, this talk will highlight similarities and differences in the temporal trends of convective cloud properties for severe (warning issued and storm report received by NWS) and non-severe convection (no warning issued or storm report received).

The satellite-derived quantity, 11-micron top of troposphere cloud emissivity (Pavolonis, 2010) is used as input into the Warning Decision Support System – Integrated Information (WDSS-II) object-tracking framework developed at the University of Oklahoma (Lakshmanan et al., 2007). The WDSS-II software is configured to create cloud objects based upon the 11-micron top of troposphere cloud emissivity field that range in size from 3-1000 infrared satellite pixels and have a top of troposphere emissivity value of at least 0.1. Within WDSS-II the various cloud objects are assigned object IDs, which are tracked with time to minimize broken tracks and allow individual cloud clusters to maintain the same unique object ID for as long as they are present in the corresponding satellite data. We apply a unique post-processing step that preserves the oldest object IDs for those clusters that overlap between consecutive satellite scans; this allows for the WDSS-II cloud clusters to maintain the same unique object ID from infancy (very small object) to convective storm maturity (large object). Finally, statistics are computed on temporal trends of the above aforementioned satellite-derived cloud properties for cloud objects of varying degrees of severity.

This work lends new insight into the temporal trends of various macro- and microphysical cloud properties of different classifications of convection from infancy to maturity. Various temporal trends may also potentially be used to predict severe deep convective development prior to the detection of a moderate intensity radar echo.